class_docstr: Independent multivariate Gaussian distribution parametrized by mean.
code: def log_prob(self, inputs, point):
      point = point.reshape(inputs.shape[:-1] + (-1,))
      (mean, std) = self._params(inputs)
      return -jnp.sum(
          # Scaled distance.
          (point - mean) ** 2 / (2 * std**2) +
          # Normalizing constant.
          (jnp.log(std) + jnp.log(jnp.sqrt(2 * jnp.pi))),
          axis=-1,
      )
  
imports: import gin
  import gym
  import numpy
  from trax import layers
  from trax.fastmath import numpy
label_desc: Use image node_id 6 for calling the Gaussian obj's underlying member method code with example usage: obj.log_prob(inputs, point) and returns: unknown